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import os
import time
import pickle
import random
from tqdm import tqdm
import argparse
import torch
from torch_geometric.loader import DataLoader
import torch.utils.data
from rdkit import RDLogger
torch.set_num_threads(5)
RDLogger.DisableLog('rdApp.*')
from utils import *
from models import Generator
from new_dataloader import DruggenDataset
from loss import generator_loss
from training_data import load_molecules
class Inference(object):
"""Inference class for DrugGEN."""
def __init__(self, config):
if config.set_seed:
np.random.seed(config.seed)
random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
os.environ["PYTHONHASHSEED"] = str(config.seed)
print(f'Using seed {config.seed}')
self.device = torch.device("cuda" if torch.cuda.is_available() else 'cpu')
# Initialize configurations
self.submodel = config.submodel
self.inference_model = config.inference_model
self.sample_num = config.sample_num
# Data loader.
self.inf_raw_file = config.inf_raw_file # SMILES containing text file for first dataset.
# Write the full path to file.
self.inf_dataset_file = config.inf_dataset_file # Dataset file name for the first GAN.
# Contains large number of molecules.
self.inf_batch_size = config.inf_batch_size
self.mol_data_dir = config.mol_data_dir # Directory where the dataset files are stored.
self.dataset_name = self.inf_dataset_file.split(".")[0]
self.max_atom = config.max_atom # Model is based on one-shot generation.
# Max atom number for molecules must be specified.
self.features = config.features # Small model uses atom types as node features. (Boolean, False uses atom types only.)
# Additional node features can be added. Please check new_dataloarder.py Line 102.
self.inf_dataset = DruggenDataset(self.mol_data_dir,
self.inf_dataset_file,
self.inf_raw_file,
self.max_atom,
self.features) # Dataset for the first GAN. Custom dataset class from PyG parent class.
# Can create any molecular graph dataset given smiles string.
# Nonisomeric SMILES are suggested but not necessary.
# Uses sparse matrix representation for graphs,
# For computational and speed efficiency.
self.inf_loader = DataLoader(self.inf_dataset,
shuffle=True,
batch_size=self.inf_batch_size,
drop_last=True) # PyG dataloader for the first GAN.
# Atom and bond type dimensions for the construction of the model.
self.atom_decoders = self.decoder_load("atom") # Atom type decoders for first GAN.
# eg. 0:0, 1:6 (C), 2:7 (N), 3:8 (O), 4:9 (F)
self.bond_decoders = self.decoder_load("bond") # Bond type decoders for first GAN.
# eg. 0: (no-bond), 1: (single), 2: (double), 3: (triple), 4: (aromatic)
self.m_dim = len(self.atom_decoders) if not self.features else int(self.inf_loader.dataset[0].x.shape[1]) # Atom type dimension.
self.b_dim = len(self.bond_decoders) # Bond type dimension.
self.vertexes = int(self.inf_loader.dataset[0].x.shape[0]) # Number of nodes in the graph.
# Transformer and Convolution configurations.
self.act = config.act
self.dim = config.dim
self.depth = config.depth
self.heads = config.heads
self.mlp_ratio = config.mlp_ratio
self.dropout = config.dropout
self.build_model()
def build_model(self):
"""Create generators and discriminators."""
self.G = Generator(self.act,
self.vertexes,
self.b_dim,
self.m_dim,
self.dropout,
dim=self.dim,
depth=self.depth,
heads=self.heads,
mlp_ratio=self.mlp_ratio,
submodel = self.submodel)
self.print_network(self.G, 'G')
self.G.to(self.device)
def decoder_load(self, dictionary_name):
''' Loading the atom and bond decoders'''
with open("DrugGEN/data/decoders/" + dictionary_name + "_" + self.dataset_name + '.pkl', 'rb') as f:
return pickle.load(f)
def print_network(self, model, name):
"""Print out the network information."""
num_params = 0
for p in model.parameters():
num_params += p.numel()
print(model)
print(name)
print("The number of parameters: {}".format(num_params))
def restore_model(self, submodel, model_directory):
"""Restore the trained generator and discriminator."""
print('Loading the model...')
G_path = os.path.join(model_directory, '{}-G.ckpt'.format(submodel))
self.G.load_state_dict(torch.load(G_path, map_location=lambda storage, loc: storage))
def inference(self):
# Load the trained generator.
self.restore_model(self.submodel, self.inference_model)
# smiles data for metrics calculation.
chembl_smiles = [line for line in open("DrugGEN/data/chembl_train.smi", 'r').read().splitlines()]
chembl_test = [line for line in open("DrugGEN/data/chembl_test.smi", 'r').read().splitlines()]
drug_smiles = [line for line in open("DrugGEN/data/akt_inhibitors.smi", 'r').read().splitlines()]
drug_mols = [Chem.MolFromSmiles(smi) for smi in drug_smiles]
drug_vecs = [AllChem.GetMorganFingerprintAsBitVect(x, 2, nBits=1024) for x in drug_mols if x is not None]
# Make directories if not exist.
if not os.path.exists("DrugGEN/experiments/inference/{}".format(self.submodel)):
os.makedirs("DrugGEN/experiments/inference/{}".format(self.submodel))
self.G.eval()
start_time = time.time()
metric_calc_dr = []
uniqueness_calc = []
real_smiles_snn = []
nodes_sample = torch.Tensor(size=[1,45,1]).to(self.device)
val_counter = 0
none_counter = 0
# Inference mode
with torch.inference_mode():
pbar = tqdm(range(self.sample_num))
pbar.set_description('Inference mode for {} model started'.format(self.submodel))
for i, data in enumerate(self.inf_loader):
val_counter += 1
# Preprocess dataset
_, a_tensor, x_tensor = load_molecules(
data=data,
batch_size=self.inf_batch_size,
device=self.device,
b_dim=self.b_dim,
m_dim=self.m_dim,
)
_, _, node_sample, edge_sample = self.G(a_tensor, x_tensor)
g_edges_hat_sample = torch.max(edge_sample, -1)[1]
g_nodes_hat_sample = torch.max(node_sample, -1)[1]
fake_mol_g = [self.inf_dataset.matrices2mol_drugs(n_.data.cpu().numpy(), e_.data.cpu().numpy(), strict=True, file_name=self.dataset_name)
for e_, n_ in zip(g_edges_hat_sample, g_nodes_hat_sample)]
a_tensor_sample = torch.max(a_tensor, -1)[1]
x_tensor_sample = torch.max(x_tensor, -1)[1]
real_mols = [self.inf_dataset.matrices2mol_drugs(n_.data.cpu().numpy(), e_.data.cpu().numpy(), strict=True, file_name=self.dataset_name)
for e_, n_ in zip(a_tensor_sample, x_tensor_sample)]
inference_drugs = [None if line is None else Chem.MolToSmiles(line) for line in fake_mol_g]
inference_drugs = [None if x is None else max(x.split('.'), key=len) for x in inference_drugs]
for molecules in inference_drugs:
if molecules is None:
none_counter += 1
with open("DrugGEN/experiments/inference/{}/inference_drugs.txt".format(self.submodel), "a") as f:
for molecules in inference_drugs:
if molecules is not None:
molecules = molecules.replace("*", "C")
f.write(molecules)
f.write("\n")
uniqueness_calc.append(molecules)
nodes_sample = torch.cat((nodes_sample, g_nodes_hat_sample.view(1,45,1)), 0)
pbar.update(1)
metric_calc_dr.append(molecules)
generation_number = len([x for x in metric_calc_dr if x is not None])
if generation_number == self.sample_num or none_counter == self.sample_num:
break
real_smiles_snn.append(real_mols[0])
et = time.time() - start_time
gen_vecs = [AllChem.GetMorganFingerprintAsBitVect(Chem.MolFromSmiles(x), 2, nBits=1024) for x in uniqueness_calc if Chem.MolFromSmiles(x) is not None]
real_vecs = [AllChem.GetMorganFingerprintAsBitVect(x, 2, nBits=1024) for x in real_smiles_snn if x is not None]
print("Inference mode is lasted for {:.2f} seconds".format(et))
print("Metrics calculation started using MOSES.")
# post-process * to Carbon atom in valid molecules
return{
"Validity": fraction_valid(metric_calc_dr),
"Uniqueness": fraction_unique(uniqueness_calc),
"Novelty (Train)": novelty(metric_calc_dr, chembl_smiles),
"Novelty (Inference)": novelty(metric_calc_dr, chembl_test),
"Novelty (AKT)": novelty(metric_calc_dr, drug_smiles),
"MaxLen": Metrics.max_component(uniqueness_calc, self.vertexes),
"MeanAtomType": Metrics.mean_atom_type(nodes_sample),
"SNN (ChEMBL)": average_agg_tanimoto(np.array(real_vecs), np.array(gen_vecs)),
"SNN (AKT)": average_agg_tanimoto(np.array(drug_vecs), np.array(gen_vecs)),
}
if __name__=="__main__":
parser = argparse.ArgumentParser()
# Inference configuration.
parser.add_argument('--submodel', type=str, default="DrugGEN", help="Chose model subtype: DrugGEN, NoTarget", choices=['DrugGEN', 'NoTarget'])
parser.add_argument('--inference_model', type=str, help="Path to the model for inference")
parser.add_argument('--sample_num', type=int, default=10000, help='inference samples')
# Data configuration.
parser.add_argument('--inf_dataset_file', type=str, default='chembl45_test.pt')
parser.add_argument('--inf_raw_file', type=str, default='DrugGEN/data/chembl_test.smi')
parser.add_argument('--inf_batch_size', type=int, default=1, help='Batch size for inference')
parser.add_argument('--mol_data_dir', type=str, default='DrugGEN/data')
parser.add_argument('--features', type=str2bool, default=False, help='features dimension for nodes')
# Model configuration.
parser.add_argument('--act', type=str, default="relu", help="Activation function for the model.", choices=['relu', 'tanh', 'leaky', 'sigmoid'])
parser.add_argument('--max_atom', type=int, default=45, help='Max atom number for molecules must be specified.')
parser.add_argument('--dim', type=int, default=128, help='Dimension of the Transformer Encoder model for the GAN.')
parser.add_argument('--depth', type=int, default=1, help='Depth of the Transformer model from the GAN.')
parser.add_argument('--heads', type=int, default=8, help='Number of heads for the MultiHeadAttention module from the GAN.')
parser.add_argument('--mlp_ratio', type=int, default=3, help='MLP ratio for the Transformer.')
parser.add_argument('--dropout', type=float, default=0., help='dropout rate')
# Seed configuration.
parser.add_argument('--set_seed', type=bool, default=False, help='set seed for reproducibility')
parser.add_argument('--seed', type=int, default=1, help='seed for reproducibility')
config = parser.parse_args()
inference = Inference(config)
inference.inference()
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